Estimating a temperature during ablation

ABSTRACT

Described embodiments include an apparatus that includes an electrical interface and a processor. The processor is configured to receive, via the electrical interface, a temperature sensed by a temperature sensor at a distal end of an intrabody probe, to estimate a temperature of tissue of a subject, based on the sensed temperature and a parameter of an ablating current driven, by an ablation electrode at the distal end of the intrabody probe, into the tissue, and to generate an output in response to the estimated temperature. Other embodiments are also described.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of, and claims the benefit of, U.S. patent application Ser. No. 14/998,204, filed Dec. 24, 2015, whose disclosure is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to invasive medical devices, and particularly to probes used in ablating tissue within the body.

BACKGROUND

Minimally-invasive intracardiac ablation is the treatment of choice for various types of arrhythmias. To perform such treatment, the physician typically inserts a catheter through the vascular system into the heart, brings the distal end of the catheter into contact with myocardial tissue in areas of abnormal electrical activity, and then energizes one or more electrodes at or near the distal end in order to create tissue necrosis.

U.S. Patent Application Publication 2010/0030209, whose disclosure is incorporated herein by reference, describes a catheter with a perforated tip, which includes an insertion tube, having a distal end for insertion into a body of a subject. A distal tip is fixed to the distal end of the insertion tube and is coupled to apply energy to tissue inside the body. The distal tip has an outer surface with a plurality of perforations through the outer surface, which are distributed circumferentially and longitudinally over the distal tip. A lumen passes through the insertion tube and is coupled to deliver a fluid to the tissue via the perforations.

U.S. Pat. No. 5,957,961, whose disclosure is incorporated herein by reference, describes a catheter having a distal segment carrying at least one electrode extending along the segment and having a number of temperature sensors arranged along the distal segment adjacent the electrode, each providing an output indicative of temperature. The catheter is coupled to a power source, which provides radiofrequency (RF) energy to the electrode. Temperature processing circuitry is coupled to the temperature sensors and the power source, and controls power output from the power source as a function of the outputs of the temperature sensors.

U.S. Pat. No. 6,312,425, whose disclosure is incorporated herein by reference, describes an RF ablation catheter tip electrode with multiple thermal sensors. A tip thermal sensor is located at or near the apex of the distal-end region, and one or more side thermal sensors are located near the surface of the proximal-end region. The electrode is preferably an assembly formed from a hollow dome-shaped shell with a core disposed within the shell. The side thermal sensor wires are electrically connected inside the shell and the core has a longitudinal channel for the side thermal sensor wires welded to the shell. The shell also preferably has a pocket in the apex of the shell, and the end thermal sensor wires pass through the core to the apex of the shell.

U.S. Pat. No. 6,217,574, whose disclosure is incorporated herein by reference, describes an irrigated split tip electrode catheter. A signal processor activates an RF generator to transmit a low level RF current to each electrode member of the split tip electrode. The signal processor receives signals indicative of the impedance between each electrode member and one or more surface indifferent electrodes and determines which electrode members are associated with the highest impedance. Such electrode members are stated to be those in greatest contact with the myocardium.

U.S. Pat. No. 6,391,024, whose disclosure is incorporated herein by reference, describes a method of assessing the adequacy of contact between an ablation electrode and biological tissue. The method measures the impedance between an ablation electrode and a reference electrode at a first and second frequencies. A percentage difference between the first-frequency impedance and the second-frequency impedance is stated to provide an indication of the state of electrode/tissue contact.

U.S. Pat. No. 6,730,077, whose disclosure is incorporated herein by reference, describes a cryocatheter for treatment of tissue. A signal conductor extends through the catheter to the catheter tip and connects to a thermally and electrically conductive shell or cap that applies an RF current to the region of tissue contacted by the tip. A tissue impedance path between the signal lead and a surface electrode mounted on the patient's skin is monitored to develop a quantitative measure of tissue contact at the distal tip.

U.S.Patent Application Publication 2014/0171936 to Govari, which is incorporated herein by reference, describes apparatus that includes an insertion tube having a distal end configured for insertion into proximity with tissue in a body of a patient and containing a lumen having an electrical conductor for conveying electrical energy to the tissue. The apparatus further includes a conductive cap attached to the distal end of the insertion tube and coupled electrically to the electrical conductor, wherein the conductive cap has an outer surface. In addition there are a multiplicity of optical fibers contained within the insertion tube, each fiber terminating in proximity to the outer surface of the cap, and being configured to convey optical radiation to and from the tissue while the electrical energy is being conveyed to the tissue.

SUMMARY OF THE INVENTION

There is provided, in accordance with some embodiments of the present invention, an apparatus that includes an electrical interface and a processor. The processor is configured to receive, via the electrical interface, a temperature sensed by a temperature sensor at a distal end of an intrabody probe, to estimate a temperature of tissue of a subject, based on the sensed temperature and a parameter of an ablating current driven, by an ablation electrode at the distal end of the intrabody probe, into the tissue, and to generate an output in response to the estimated temperature.

In some embodiments, the parameter includes a power of the ablating current.

In some embodiments, the parameter includes an amplitude of the ablating current.

In some embodiments, the processor is configured to estimate the temperature of the tissue by selecting a coefficient in response to the parameter, and estimating the temperature of the tissue by multiplying, by the coefficient, a value that is based on the sensed temperature.

In some embodiments, the processor is configured to estimate the temperature of the tissue as T₀+α(T_(I)−T₀), T_(I) being the sensed temperature, α being the coefficient, and T₀ being a quantity based on a temperature T_0 that was sensed by the temperature sensor prior to the driving of the ablating current.

In some embodiments, the processor is further configured to compute T₀ as T_0−β, β being a correction factor that depends on a stability of temperatures sensed by the temperature sensor prior to the driving of the ablating current.

In some embodiments, the processor is configured to select the coefficient by computing the coefficient.

In some embodiments, the processor is configured to compute the coefficient by substituting the parameter into a closed-form expression for the coefficient.

There is further provided, in accordance with some embodiments of the present invention, a method that includes receiving, by a processor, a temperature sensed by a temperature sensor at a distal end of an intrabody probe. The method further includes estimating, by the processor, a temperature of tissue of a subject, based on the sensed temperature and a parameter of an ablating current driven, by an ablation electrode at the distal end of the intrabody probe, into the tissue, and generating an output in response to the estimated temperature.

There is further provided, in accordance with some embodiments of the present invention, a method that includes, using a probe that includes an ablation electrode, at least one internal temperature sensor inside the ablation electrode, and at least one surface temperature sensor at a surface of the ablation electrode, performing a plurality of ablations of tissue with different respective amplitudes of an ablating current. The method further includes, during each of the ablations, sensing an internal temperature, using the internal temperature sensor, and a surface temperature, using the surface temperature sensor, and, using a processor, learning a relationship between the sensed internal temperature and the sensed surface temperatures.

In some embodiments, the relationship is expressed by an equation T_(S)=T₀+α(T_(I)−T₀), α being a coefficient, T_(S) being the sensed surface temperature, T_(I) being the sensed internal temperature, and T₀ being a baseline temperature, and learning the relationship includes learning the relationship by learning a dependency of α on the amplitude of the ablating current.

In some embodiments, learning the dependency of α on the amplitude of the ablating current includes learning the dependency by regressing (T_(S)−T₀)/(T_(I)−T₀) on the amplitude of the ablating current.

In some embodiments, regressing (T_(S)−T₀)/(T_(I)−T₀) on the amplitude of the ablating current includes quadratically regressing (T_(S)−T₀)/(T_(I)−T₀) on the amplitude of the ablating current.

The present invention will be more fully understood from the following detailed description of embodiments thereof, taken together with the drawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic pictorial illustration of a system for cardiac ablation treatment, in accordance with some embodiments of the present invention;

FIG. 2 shows experimental data acquired by the present inventors;

FIG. 3A is a flow diagram for a method for learning a coefficient, in accordance with some embodiments of the present invention; and

FIG. 3B is a flow diagram for a method for estimating a temperature of tissue, in accordance with some embodiments of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS Overview

It has been found that cooling (or “irrigating”) the area of the ablation site reduces thrombus (blood clot) formation. For this purpose, for example, Biosense Webster Inc. offers the ThermoCool® irrigated-tip catheter for use with its CARTO® integrated mapping and ablation system. The metal catheter tip, which is energized with radio-frequency (RF) electrical current to ablate the tissue, has a number of peripheral holes, distributed circumferentially around the tip, for irrigation of the treatment site. During the procedure, a pump coupled to the catheter delivers an irrigating saline solution to the catheter tip, and the solution flows out through the holes. (In some embodiments, even while no ablating current is being passed into the tissue, the flow of irrigating fluid is maintained, e.g., at a reduced flow rate.)

When performing an ablation procedure, it is often advantageous to position one or more temperature sensors near the tissue that is being ablated, to help provide feedback to the operating physician. For example, if the temperature sensors sense that the tissue is being overheated, the operating physician may stop the ablation procedure or modify ablation parameters.

At least in some cases, to measure the temperature at the tissue-electrode interface as accurately as possible, the temperature sensors would ideally be positioned such that they contact the tissue. However, due to regulatory concerns, and/or for other reasons, contacting the tissue with the temperature sensors may not be feasible. Hence, a particular challenge, when sensing the temperature of the tissue, is that a sensor that is not in contact with the tissue may sense a temperature that is lower than the actual temperature of the tissue at the tissue-electrode interface. Furthermore, regardless of whether the sensors are in contact with the tissue, the flow of irrigating fluid (e.g., saline) from the ablation electrode may cause the sensors to sense a temperature that is lower than that which the sensors would have otherwise sensed. For example, the irrigating fluid may function as a heat sink, transferring heat away from the temperature sensors.

Embodiments of the present invention address these challenges, by providing methods and apparatus for estimating a temperature of the tissue, at least at the tissue-electrode interface, based at least on the sensed temperature and on the flow rate of the irrigating fluid.

In some embodiments, the estimation is based on a parameter of the ablating current, alternatively or additionally to the fluid flow rate. For example, some embodiments take advantage of the fact that, typically, the ablation system is configured to operate in accordance with the relevant Instructions for Use (IFU), which specify a particular fluid flow rate that, for safety reasons, must be maintained for any particular amplitude of the ablating current. (As the amplitude increases, so does the required flow rate.) Given this deterministic relationship between the ablating-current amplitude and the fluid flow rate, it is possible to estimate the temperature of the tissue based on the ablating-current amplitude, even without explicitly ascertaining the flow rate. An advantage of such embodiments is that the ablating-current amplitude is typically easier to ascertain than is the flow rate.

For example, during the ablation procedure, a processor may receive (e.g., from a dongle that is connected to the RF generator that supplies the ablating current) the amplitude I_(C) of the ablating current. The processor may then compute a coefficient α, by substituting I_(C) into a formula that expresses a as a function of I_(C). The processor may then use this coefficient α to estimate the temperature of the tissue from the sensed temperature that was received from the temperature sensor. For example, the processor may estimate the temperature of the tissue as T₀+α(T_(I)−T₀), where T_(I) is the sensed temperature and T₀ is a baseline temperature.

Embodiments of the present invention further include systems and methods for learning α as a function of I_(C). For example, in some embodiments, an ablation catheter is specially fitted with temperature sensors at the surface of the ablation electrode, referred to herein as surface temperature sensors. This specially-fitted catheter is then used to perform a plurality of test ablations (typically on ex vivo tissue) with varying ablating-current amplitudes. During each of these test ablations, the internal temperature sensors inside of the ablation electrode measure the internal temperature inside of the ablation catheter, while the surface temperature sensors measure the temperature at the interface between the electrode and the tissue. The sensed temperatures and actual temperatures are then used to learn the formula for α.

System Description

Reference is initially made to FIG. 1, which is a schematic pictorial illustration of a system 20 for cardiac ablation treatment, in accordance with an embodiment of the present invention. An operating physician 28 (such as an interventional cardiologist) inserts an intra-body probe, such as a catheter 22, via the vascular system of a patient 26, into a chamber of the patient's heart 24. For example, to treat atrial fibrillation, the operating physician may advance the catheter into the left atrium and bring a distal end 30 of the catheter into contact with myocardial tissue that is to be monitored and/or ablated.

Catheter 22 is connected at its proximal end, via an electrical interface 50, to a console 32. Console 32 comprises an RF energy generator 34, which supplies electrical power via catheter 22 to distal end 30 in order to ablate the target tissue. A processor 52 tracks the temperature of the tissue at distal end 30 by processing the outputs of temperature sensors in the distal end, as described below. An irrigation pump 38 supplies an irrigating fluid, such as saline solution, through catheter 22 to distal end 30. In addition, in some embodiments, an optical module 40 provides optical radiation, typically from, but not limited to, a laser, an incandescent lamp, an arc lamp, or a light emitting diode (LED), for transmission from distal end 30 to the target tissue. The module receives and analyzes optical radiation returning from the target tissue and acquired at the distal end.

On the basis of information provided by the temperature sensors and/or optical module 40, processor 52 may control the power applied by RF energy generator 34 and/or the flow of fluid provided by pump 38, either automatically or in response to inputs (e.g., from the operating physician), as further described hereinbelow.

System 20 may be based on the above-mentioned CARTO system, for example, which provides extensive facilities to support navigation and control of catheter 22.

Distal end 30 of catheter 22 includes an ablation electrode 46, which includes a distal face 58. Typically, when performing the ablation, a portion of ablation electrode 46 (e.g., distal face 58) is brought into contact with (e.g., pressed against) the tissue that is to be ablated, and subsequently, radiofrequency energy, supplied by RF energy generator 34, is applied to the tissue by the ablation electrode. As shown in FIG. 1, ablation electrode 46 may be shaped to define a plurality of perforations 60. During the procedure, irrigating fluid, supplied by irrigation pump 38, is passed from perforations 60. The passing of the irrigating fluid may help prevent blood clots from forming, by cooling and diluting the blood in the vicinity of the ablation site.

As shown in the figure, a plurality of temperature sensors 48 (e.g., thermocouples) are disposed at various respective positions within ablation electrode 46. In particular, the “head-on” view of distal face 58 shows three circumferentially-arranged temperature sensors 48 near the distal face 58 of the electrode, each of the temperature sensors being contained within a lumen in the wall of the electrode. The isometric view of distal end 30, which “cuts away” the outer wall of one of the lumens, shows two temperature sensors within the lumen—(i) a distal temperature sensor 48 a, which is one of the three sensors shown in the distal-end view, and (ii) a proximal temperature sensor 48 b, which is one of three proximal sensors that are not shown in the distal-end view. Distal end 30, as shown in FIG. 1, thus comprises a total of six temperature sensors. (Notwithstanding the above, it is noted that the scope of the present disclosure includes the use of any suitable number and arrangement of temperature sensors.)

While the ablation electrode is used to drive an ablating current into the tissue, and while the irrigating fluid is passed from the distal end of the catheter (e.g., through perforations 60), one or more of the temperature sensors are used to sense respective temperatures.

In general, it is advantageous to have a plurality of temperature sensors disposed at various locations with respect to the tissue, e.g., in that information regarding the orientation of the ablation electrode may be deduced from the various temperature readings provided by the sensors. For example, if each of the three distal sensors senses approximately the same temperature (indicating that the three distal sensors are approximately equidistant from the tissue), and/or if each of the three proximal sensors senses approximately the same temperature (indicating that the three proximal sensors are approximately equidistant from the tissue), it may be deduced that the electrode is oriented perpendicularly with respect to tissue, as is typically desired. Conversely, if, for example, one of the proximal sensors senses a temperature that is higher than that sensed by the other two proximal sensors, it may be deduced that the ablation electrode is not oriented perpendicularly with respect to the tissue, such that one of the proximal sensors is closer to the tissue than the other proximal sensors.

Aside from providing information concerning the orientation of the catheter, the temperature sensors may facilitate the performance of the ablation, by indicating whether the tissue at the tissue-electrode interface is at the desired temperature for ablation. However, as noted above, a temperature sensor that is not in contact with the tissue may sense a temperature that is lower than the actual temperature of the tissue at the tissue-electrode interface. For example, distal sensor 48 a may be disposed somewhat proximally to distal face 58, such that distal sensor 48 a is generally not in contact with the tissue during the ablation procedure. Consequently, the temperature sensed by distal sensor 48 a is typically lower than the actual temperature of the tissue at the interface. The difference between the actual temperature and the sensed temperature is typically even greater for proximal sensor 48 b, which is farther from the tissue than distal sensor 48 a.

Furthermore, as noted above, the flow of irrigating fluid from perforations 60 causes the respective sensed temperatures from at least some of the temperature sensors to be lower, relative to if no irrigating fluid were flowing from perforations 60. To address the above challenges, embodiments of the present invention provide apparatus and methods for estimating the actual temperature of the tissue, at least at the tissue-electrode interface, as described hereinbelow.

In general, processor 52 may be embodied as a single processor, or as a cooperatively networked or clustered set of processors. Processor 52 is typically a programmed digital computing device comprising a central processing unit (CPU), random access memory (RAM), non-volatile secondary storage, such as a hard drive or CD ROM drive, network interfaces, and/or peripheral devices. Program code, including software programs, and/or data are loaded into the RAM for execution and processing by the CPU and results are generated for display, output, transmittal, or storage, as is known in the art. Such program code and/or data, when provided to the processor, produce a machine or special-purpose computer, configured to perform the tasks described herein.

Learning the Relationship Between the Sensed Temperature and the Actual Temperature

Reference is now made to FIG. 2, which shows experimental data acquired by the present inventors. As further described below, the experimental data of FIG. 2 shows the relationship between the temperature sensed by the temperature sensors and the “actual” measured temperature of the tissue.

To acquire the data, distal end 30 was used to “ablate” ex vivo tissue multiple times. During each of the trial ablations, irrigating fluid was pumped out of the distal end, multiple temperature sensors in the distal end of the catheter were used for sensing, and additionally, a thermometer was used to measure the actual temperature of the tissue at the tissue-electrode interface. Two sets of trial ablations were conducted; a first set with an irrigating-fluid flow rate of 8 mL/min, and a second set with an irrigating-fluid flow rate of 15 mL/min. The trial ablations of each set were conducted with different respective ablation powers, and/or different respective forces of contact between the electrode and the tissue. (Each of these factors affects the temperature at the tissue-electrode interface; for example, increasing the power, and/or increasing the force of contact, increases the temperature.)

The sensed temperature values, which are also referred to herein as “internal temperature” values, are indicated by the notation T_(I). These values, minus a normalizing temperature T_0 (described below), are plotted along the horizontal axis of FIG. 2. (In this particular case, each of the sensed temperature values T_(I) is the average of the temperatures sensed by the three distal temperature sensors shown in FIG. 1.) The thermometer readings indicate the “surface temperature” at the surface of the ablation electrode, and are indicated by the notation T_(S). These readings, minus T_0, are plotted along the vertical axis. Each point in FIG. 2 thus represents a pair of values (T_(I)−T_0, T_(S)−T_0) for a particular flow rate, ablation power, and force of contact. (Typically, a flow rate of 15 mL/min is used only for a relatively high ablation power and/or force of contact; hence, the data for 15 mL/min includes only relatively high temperatures.)

As shown in FIG. 2, for each of the flow rates, a linear regression function was fit to the acquired data with a high goodness of fit, as evidenced by the high “R-squared” values. This regression function may be expressed in the form T_(S)−T_0=α(T_(I)−T_0), where T_0, T_(I), and T_(S) are as described above, and α is a coefficient that is a function of the flow rate of the irrigating fluid. In particular, for a flow rate of 8 mL/min, FIG. 2 shows a coefficient α of around 1.6, while for a flow rate of 15 mL/min, FIG. 2 shows a coefficient α of around 2.

Typically, T_0 is the value of T_(I) immediately prior to the start of the ablation, e.g., the average temperature sensed over the one second prior to the start of the ablation. Prior to the start of the ablation, the catheter begins to deliver irrigating fluid to the subject's blood. If the fluid flows through the ablation electrode for long enough before the start of the ablation, a “steady state” is attained, whereby T_(S) is the same as T_(I), such that T_(I)=T_(S)=T_0. Hence, the subtraction of T_0 from each of T_(I) and T_(S), prior to performing the regression, typically simplifies the regression, by causing each of the regressed lines to pass through the origin. Stated differently, the regression is simplified, in that the regression function includes only one variable (i.e., α), rather than two variables. Notwithstanding the above, it is noted that the regressions depicted in FIG. 2 may be performed even without measuring or using T_0; the measurement and use of T_0 is generally for convenience only.

In any case, the independent variable in the regression is typically a variable that is based on T_(I). For example, this variable may be T_(I) itself, or T_(I)−T_0, as described above. Similarly, the dependent variable in the regression is typically a variable that is based on T_(S). For example, this variable may be T_(S) itself, or T_(S)−T_0, as described above.

As further described hereinbelow, the regression function illustrated in FIG. 2 may be used to estimate the temperature of the tissue, at least at the tissue-electrode interface, during a live ablation procedure.

As noted above, the trials depicted in FIG. 2 were conducted with T_(S) measured at the electrode-tissue interface. In some cases, during a live procedure, it may be advantageous to estimate the temperature of the tissue at deeper locations within the tissue, e.g., 5 mm beneath the tissue. Hence, the scope of the present invention includes (i) performing ablations (e.g., trial ablations) with the temperature T_(S) measured at such deeper locations, thus allowing respective regression functions to be determined for these locations, and (ii) during a live procedure, using the regression functions to estimate the temperature of the tissue at these locations.

In some embodiments, the flow of irrigation fluid roughly affects a subset of, or all of, the temperature sensors in a similar way, such that α may be learned by averaging the sensed temperatures over the subset of, or all of, the sensors. For example, as noted above, the sensed temperatures shown in FIG. 2 are averages for the three distal sensors, and a single α is learned for the three distal sensors. In other embodiments, α may be learned separately for each of one or more of the sensors. FIG. 3A, described immediately hereinbelow, describes such an embodiment.

Reference is now made to FIG. 3A, which is a flow diagram for a method 62 for learning α, in accordance with some embodiments of the present invention. In method 62, α is learned for one or more flow rates, for each of one or more temperature sensors. For each sensor and flow rate, α is learned, at a learning step 64, using the technique described above with reference to FIG. 2. In other words, at learning step 64, distal end 30 is used to “ablate” ex vivo tissue using various ablation powers and/or contact forces, while irrigating fluid is pumped out of the distal end. Sensed temperatures and actual temperatures are acquired, and processor 52 then applies regression (e.g., linear regression) to learn α.

In general, since the flow rate of the irrigation fluid may vary over different ablation procedures, and/or may vary during a single ablation procedure, it may be advantageous to learn α for more than one flow rate. For example, various flow rates within the range of 8-15 mL/min may be of interest, since, during a live procedure, the flow rate is typically between 8 mL/min and 15 mL/min.

For example, a may first be learned, at learning step 64, for sensor 48 a (FIG. 1) and a flow rate of 8 mL/min. Subsequently, at a first decision step 66, a decision is made as to whether to change the current flow rate. If the decision is made to change the current flow rate (e.g., to 15 mL/min), the current flow rate is changed, at a flow-rate-changing step 67. Subsequently, at learning step 64, α is learned for the second flow rate.

Once α has been learned for all of the flow rates of interest, method 62 proceeds to a second decision step 68, at which a decision is made as to whether to change the current sensor. If a decision is made to change the current sensor (e.g., to sensor 48 b (FIG. 1)), the sensor is changed at a sensor-changing step 69. Subsequently, at learning step 64, α is learned for the second sensor, for all of the flow rates of interest.

Method 62 ends once a has been learned for all of the sensors and flow rates of interest.

Several alternate embodiments of method 62 will now be described.

(i) In some embodiments, as described above in the Overview, the estimation of the tissue temperature is based on a parameter of the ablating current, such as the amplitude or power of the ablating current, alternatively or additionally to being based on the flow rate. (The power of the ablating current may be referred to as the “ablation power”.) In such embodiments, α is dependent on an ablating-current parameter (e.g., the amplitude I_(C) of the ablating current), alternatively or additionally to the flow rate. Method 62 may therefore be adapted to learn the dependency of α on the ablating-current parameter. For example, in some embodiments, as described above in the Overview, the flow rate is a deterministic function of I_(C), such that α is a function only of I_(C). In such embodiments, a plurality of trial ablations at different respective values of I_(C) may be performed, and a may then be learned by regressing the quantity (T_(S)−T_0)/(T_(I)−T_0) on I_(C). This yields an equation that expresses α as a function of I_(C).

(ii) In some embodiments, as described above in the Overview, a “test probe,” which comprises an ablation electrode 46 (FIG. 1) outfitted with one or more surface temperature sensors at its surface, is manufactured. This probe may be used to perform a plurality of trial ablations, during which one or more parameters, such as I_(C) and/or the flow rate, are varied. During each of these trial ablations, both the surface temperature T_(S), measured by the surface temperature sensors, and the internal temperature T_(I), measured by the internal temperature sensors within the ablation electrode, may be recorded. α may then be learned from the values of T_(S) and T_(I), as described above.

It is noted that the surface temperature sensors need not necessarily be exposed. For example, a thin layer of the distal face of the ablation electrode may cover these sensors. (Such covered sensors are nevertheless referred to herein as “surface” sensors, given that the temperature sensed by these sensors is effectively the temperature at the distal face of the ablation electrode.)

(iii) In some embodiments, an adjustment is made to the normalizing temperature T_0. This adjustment accounts for the fact that the above-described steady state, during which the surface temperature is the same as the sensed temperature, is not necessarily reached prior to the start of an ablation. The adjusted normalizing temperature may be referred to as a baseline temperature T₀.

In some embodiments, for example, T₀ is calculated by subtracting a correction factor β from T_0, i.e., T₀ is computed as T_0−β, where β depends on the stability of temperatures sensed by the temperature sensor prior to the driving of the ablating current. For example, β may depend on the difference D=M2−M1, where M2 is the average of the last K temperature readings prior to the start of the ablation, and M1 is the average of the last L temperature readings prior to the start of the ablation, where L<K. (For example, K may be between 30 and 40, with L between 5 and 15.) If D is below a particular threshold, β is assigned a value of 0, since the low value of D indicates that the steady state has been reached. Otherwise, β is assigned a value greater than 0. For example, β may increase linearly as a function of D, from 0 up to a particular cap.

Advantageously, any one of embodiments (i), (ii), and (iii) may be combined with any other one of these embodiments. For example, the following is a description of an embodiment that combines all of (i), (ii), and (iii).

First, as described above, a test probe is manufactured, this probe having an ablation electrode outfitted with one or more surface temperature sensors at its surface. For example, one or more (e.g., three) holes may be drilled in the ablation electrode, and a surface temperature sensor may be placed in each of these holes. Aside from the surface temperature sensors, this probe is typically similar, or identical, to catheter 22. For example, the test probe typically has one or more internal temperature sensors 48 inside the ablation electrode, e.g., as described above with respect to FIG. 1.

Next, using the test probe, a plurality of trial ablations of tissue (typically, ex vivo tissue) are performed, with different respective ablating-current amplitudes. During each of the ablations, the internal temperature sensors sense the internal temperature T_(I) within the ablation electrode, while the surface temperature sensors sense the surface temperature T_(S) at the surface of the ablation electrode (which is at the electrode-tissue interface). Then, the relationship between the sensed internal temperature and the sensed surface temperature, which may be expressed by the equation T_(S)=T₀+α(T_(I)−T₀), is learned. (As described above, T_(S) is the sensed surface temperature, T_(I) is the sensed internal temperature, T₀ is the baseline temperature, and α is a coefficient.)

Effectively, the relationship is learned by learning the dependency of α on the relevant parameter(s). For example, assuming that α is a function of only the ablating-current amplitude I_(C), the relationship may be learned by learning a dependency of α on I_(C). Typically, this dependency is learned by processor 52, by regressing the quantity (T_(S)−T₀)/(T_(I)−T₀) on I_(C), which, as described above, varies between the trial ablations. Typically, the regression is quadratic, such that α is learned to be a function of I_(C) ².

For example, the following table shows data acquired from a set of trial ablations:

TABLE 1 Trial Ablation # I_(C) (A) (T_(S) − T₀)/(T_(I) − T₀) 1 0.42 1.4 2 0.56 1.48 3 0.61 1.59 4 0.66 1.79 5 0.7 1.74 6 0.78 1.75

Performing a quadratic regression on these data yields the equation α=(T_(S)−T₀)/(T_(I)−T₀)=0.9838I_(C) ²+1.2277.

For cases in which there is more than one internal temperature sensor, T_(I)−T₀ may be calculated as a function of the set of values {T_(I) ^(i)−T₀ ^(i)}, where T_(I) ^(i) is the temperature measured by the i^(th) internal temperature sensor, and T₀ ^(i) is the corresponding baseline temperature. For example, T_(I)−T₀ may be calculated as the average or maximum of {T_(I) ^(i)−T₀ ^(i)}. Similarly, in the event of multiple surface temperature sensors, T_(S)−T₀ may be calculated as an average or maximum of the set of values {T_(S) ^(i)−T₀ ^(i)}.

Estimating the Tissue Temperature

Reference is again made to FIG. 1, and is additionally made to FIG. 3B, which is a flow diagram for a method 49 for estimating a temperature of tissue, in accordance with some embodiments of the present invention. Whereas method 62 is typically (but not necessarily) practiced ex vivo and “offline,” method 49 is practiced in vivo, during a live ablation procedure. Method 49 uses the information learned from method 62 to estimate the tissue temperature.

Method 49 begins with an initial-sensing step 70, at which T_0 is sensed. (Typically, an average over several of the sensors is used for T_0.) In some embodiments, at a baseline-calculating step 71, a baseline temperature T₀ is calculated from T_0, e.g., by subtracting a correction factor β from T_0, as described above. Subsequently, at an ablation-beginning step 74, the operating physician begins to perform the ablation.

As shown in FIG. 1 and noted above, system 20 typically comprises an electrical interface 50 (e.g., a connector, port, or dongle), which connects the proximal end of catheter 22 to processor 52. Signals received from the distal end of the catheter (e.g., via one or more wires running through the catheter) are passed to the processor via interface 50, and, conversely, signals from the processor are passed to the distal end of the catheter via interface 50. Thus, via interface 50, processor 52 may receive, at a receiving step 51, the respective temperatures sensed by sensors 48 during the ablation procedure. The processor may then average these temperatures over a subset of the sensors or over all of the sensors, or may instead compute the maximum of the temperatures. This average or maximum is used as the T_(I) value for estimating the tissue temperature, as described below.

In some embodiments, interface 50 performs the averaging or computation of the maximum, and then, at receiving step 51, communicates the average or maximum to the processor, without necessarily communicating all of the sensed temperatures to the processor.

In some embodiments, the processor further receives, at receiving step 51, the fluid flow rate of the irrigating fluid, e.g., by receiving the fluid flow rate directly from pump 38. Alternatively or additionally, the processor may receive, at receiving step 51, a parameter of, such as the amplitude and/or power of, the ablating current that is output from RF energy generator 34. (This parameter may be received from interface 50.) In other embodiments, as described below, the processor controls the pump and/or the RF generator, such that the processor generally “knows” the fluid flow rate and/or the ablating-current parameter even without the performance of receiving step 51.

Subsequently, at a coefficient-selecting step 76, the processor selects (i.e., computes, or selects from a lookup table) the appropriate coefficient α, in response to the flow rate and/or ablating-current parameter. Then, at an estimation step 53, the processor estimates the temperature of the tissue in the vicinity of electrode 46 (e.g., at the surface of the electrode, i.e., at the tissue-electrode interface), based at least on the sensed temperature T_(I) and the selected coefficient α, which is a function of the fluid flow rate of the irrigating fluid and/or the parameter of the ablating current. For example, the processor may compute the estimated temperature T_(S)′ of the tissue, by applying the equation T_(S)′=α(T_(I)−T₀)+T₀. In other words, the processor may multiply T_(I)−T₀ by the selected α, and then add T₀, to arrive at the estimated temperature. (If T_(S)′ is less than T_(I), such as in the event that T_(I) is less than T₀ due to a significant cooling effect from the irrigating fluid, the processor typically returns T_(I) as the estimated temperature.)

In some embodiments, a model is fit to the experimentally-derived values of α. In such embodiments, a coefficient α that is interpolated from the experimentally-derived coefficients may be selected for the temperature estimation. For example, using linear interpolation, for the values shown in FIG. 2, the selected α would be approximately 1.7 for a flow rate of 10 mL/min. Alternatively, extrapolation may be used to select α. In yet other embodiments, as described above, method 62 yields a closed-form expression for α (e.g., of the form α=a*I_(C) ²+b), such that α may be readily computed by substituting the relevant parameter(s) (such as I_(C)) into the closed-form expression.

In some embodiments, the processor performs respective estimates for a plurality of subsets of the temperature sensors, and then averages the respective estimates to arrive at a “combined” estimate. For example, with reference to FIG. 1, the processor may perform a first estimate for the three distal sensors and a second estimate for the three proximal sensors, and then compute the combined estimate by averaging the two separate estimates.

Subsequently, in response to the estimated temperature (e.g., the combined estimate), at an output-generating step 55, the processor generates an output, such as a visual output 57 that indicates the estimated temperature. (Visual output 57 may be shown on a user interface 56, which includes, for example, a touch screen.) In response to the output, operating physician 28 may adjust the power of the ablating current supplied by RF energy generator 34, e.g., by stopping the ablating current, or by otherwise decreasing the power of the current. Alternatively or additionally, in response to the output, the operating physician may change the rate of flow of irrigating fluid supplied by pump 38, or change the contact force with which the electrode is pressed against the tissue.

In some embodiments, the operating physician controls RF energy generator 34 and/or pump 38 via processor 52. In such embodiments, the operating physician typically provides input to the processor, such as by using user interface 56. In response to the input, the processor generates a control signal 59 that controls the RF energy generator and/or the pump. In other embodiments, processor 52 automatically controls the RF energy generator and/or pump, i.e., the output that is generated in output-generating step 55 includes control signal 59.

Typically, method 49 is repeatedly performed during the ablation procedure, i.e., steps 51, 76, 53, and 55 are repeatedly performed in sequence, such that patient 26 is continually monitored during the procedure.

It will be appreciated by persons skilled in the art that the present invention is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present invention includes both combinations and subcombinations of the various features described hereinabove, as well as variations and modifications thereof that are not in the prior art, which would occur to persons skilled in the art upon reading the foregoing description. Documents incorporated by reference in the present patent application are to be considered an integral part of the application except that to the extent any terms are defined in these incorporated documents in a manner that conflicts with the definitions made explicitly or implicitly in the present specification, only the definitions in the present specification should be considered. 

1. Apparatus, comprising: an electrical interface; and a processor, configured: to receive, via the electrical interface, a temperature sensed by a temperature sensor at a distal end of an intrabody probe, to estimate a temperature of tissue of a subject, based on the sensed temperature and a parameter of an ablating current driven, by an ablation electrode at the distal end of the intrabody probe, into the tissue, and to generate an output in response to the estimated temperature.
 2. The apparatus according to claim 1, wherein the parameter includes a power of the ablating current.
 3. The apparatus according to claim 1, wherein the parameter includes an amplitude of the ablating current.
 4. The apparatus according to claim 1, wherein the processor is configured to estimate the temperature of the tissue by: selecting a coefficient in response to the parameter, and multiplying, by the coefficient, a value that is based on the sensed temperature.
 5. The apparatus according to claim 4, wherein the processor is configured to estimate the temperature of the tissue as T₀+α(T_(I)−T₀), T_(I) being the sensed temperature, α being the coefficient, and T₀ being a quantity based on a temperature T_0 that was sensed by the temperature sensor prior to the driving of the ablating current.
 6. The apparatus according to claim 5, wherein the processor is further configured to compute T₀ as T_0−β, β being a correction factor that depends on a stability of temperatures sensed by the temperature sensor prior to the driving of the ablating current.
 7. The apparatus according to claim 4, wherein the processor is configured to select the coefficient by computing the coefficient.
 8. The apparatus according to claim 7, wherein the processor is configured to compute the coefficient by substituting the parameter into a closed-form expression for the coefficient.
 9. A method, comprising: receiving, by a processor, a temperature sensed by a temperature sensor at a distal end of an intrabody probe; estimating, by the processor, a temperature of tissue of a subject, based on the sensed temperature and a parameter of an ablating current driven, by an ablation electrode at the distal end of the intrabody probe, into the tissue; and generating an output in response to the estimated temperature.
 10. The method according to claim 9, wherein the parameter includes a power of the ablating current.
 11. The method according to claim 9, wherein the parameter includes an amplitude of the ablating current.
 12. The method according to claim 9, wherein estimating the temperature of the tissue comprises: selecting a coefficient in response to the parameter, and estimating the temperature of the tissue by multiplying, by the coefficient, a value that is based on the sensed temperature.
 13. The method according to claim 12, wherein estimating the temperature of the tissue comprises estimating the temperature of the tissue as T₀+α(T_(I)−T₀), T_(I) being the sensed temperature, α being the coefficient, and T₀ being a quantity based on a temperature T_0 that was sensed by the temperature sensor prior to the driving of the ablating current.
 14. The method according to claim 13, further comprising computing T₀ as T_0−β, β being a correction factor that depends on a stability of temperatures sensed by the temperature sensor prior to the driving of the ablating current.
 15. The method according to claim 12, wherein selecting the coefficient comprises selecting the coefficient by computing the coefficient.
 16. The method according to claim 15, wherein computing the coefficient comprises computing the coefficient by substituting the parameter into a closed-form expression for the coefficient.
 17. A method, comprising: using a probe that includes an ablation electrode, at least one internal temperature sensor inside the ablation electrode, and at least one surface temperature sensor at a surface of the ablation electrode, performing a plurality of ablations of tissue with different respective amplitudes of an ablating current; during each of the ablations, sensing an internal temperature, using the internal temperature sensor, and a surface temperature, using the surface temperature sensor; and using a processor, learning a relationship between the sensed internal temperature and the sensed surface temperatures.
 18. The method according to claim 17, wherein the relationship is expressed by an equation T_(S)=T₀+α(T_(I)−T₀), α being a coefficient, T_(S) being the sensed surface temperature, T_(I) being the sensed internal temperature, and T₀ being a baseline temperature, and wherein learning the relationship comprises learning the relationship by learning a dependency of α on the amplitude of the ablating current.
 19. The method according to claim 18, wherein learning the dependency of α on the amplitude of the ablating current comprises learning the dependency by regressing (T_(S)−T₀)/(T_(I)−T₀) on the amplitude of the ablating current.
 20. The method according to claim 19, wherein regressing (T_(S)−T₀)/(T_(I)−T₀) on the amplitude of the ablating current comprises quadratically regressing (T_(S)−T₀)/(T_(I)−T₀) on the amplitude of the ablating current. 